Transcript Maltego Radium - Kansas State University

Shalin Hai-Jew
Kansas State University
Conference on Higher Education Computing in Kansas (CHECK)
May 29 – 30, 2013, Pittsburg State University, Pittsburg, Kansas
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Maltego Radium™ (v. 3.3.3; v. 1 in 2008) is a penetration testing
tool that collects public data about organizations, websites, and
identities, for awareness of social and technological presence
across the Internet. The tool’s interface is highly usable and
interactive. The tool enables a deep dive analysis into the
interrelationships online, and it extends the “knowability” of
electronic identities. This tool enables explorations of emails,
telephone numbers, websites, organizations, by offering access to
information that would often be “invisible” otherwise. The visual
outputs are interactive and include half-a-dozen visualizations in a
social network (node-link) format. The presentation will show
how to conduct “machines” and “transforms” of a target, how to
visually map the data, and how to analyze it.
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People at some point will have linked their pseudonyms with realworld personally identifiable information (PII)
People act on interests (which are expressed in some way
electronically), and their interests reveal something about the
unknown node
People’s online relationships can identify an unknown node based
on the connections, power relationships, intercommunications,
and the external identities
All online actions can be linked to geographical locations, and
those locations may be revealing
Knowability of an unknown node / entity (or group) is increased
when a collective and comprehensive electronic footprint is
rendered
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Hi! Who are you, and what are your interests
re: the topic? Anyone ever use a “hacking”
tool? If so, what?
Do you have an idea for a Maltego Radium™
“machine” or “transform” run that you want
us to try during this session? (I’ll ask you near
the end of the presentation.)
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Electronic Network Analysis:
People
Content
Technologies
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People often interact in homophilous (preferential selection based
on similarities with the self or the in-group; assortative mixing) or
heterophilous (preferential selection by difference; disassortative
mixing) ways
 Depending on the non-kin social context (such as work-based,
volunteer-based, romance-based, friendship-based, hobby-based, or
others)
People find meaning and identity in ways similar to those that are
close to them (the “company you keep” assumption); yet, people’s
identities in this age are not necessarily coherent and unified but
are fragmented and multiple and experimental
 World is socially constructed in various types of hierarchies
(structurally)
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 Resources and information (and inter-exchanges) move through these
hierarchies through particular social paths
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Electronic socio-technical spaces (STS)
somewhat mirror the real world but not 1-1
(or even close); called the “cyber-physical
confluence”
 Electronic data may be used to make some
cautious extrapolations (or informal intuitions or
“whispers”) about real-world off-line
personalities, values, and actions
 Social network analysis (SNA) data are used with
other information to set a full(er) context
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Electronic spaces offer empirical in vivo (in-field) relational
information (based on actual links, actual connections,
and actual relationships based on electronic
documentation) that is behavior- and action-based and
not professed only
 May include “big data” analyses of entire datasets of complete
networks
 May include cross-references between numbers of data sets
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Strength of inter-relationships is critical based on
interaction patterns
 Complex statistics and layout algorithms are used to express
relationships in social network analysis
 Radically different visualizations may be possible depending on
the layout algorithms
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What moves through network topologies (digital
information, resources, influence and
socialization, and memes, etc.) is also important
to understand and analyze
 Machine-analyzed computerized sentiment analysis
(through text mining) is one way to evaluate
messages moving through virtual communities
 Word frequency counts is another machine-based
way to evaluate messages
 Image analysis is another way to evaluate message
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Graphs built from graph metrics, which describe structural
aspects of the network (such as numbers of nodes and
links, types of connections, density or sparseness of ties,
leadership and role types, motif censuses, and other
factors)
 Graphs as 2D spaces
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 Not x or y axes but about relationships between the nodes and
the links
 Can lay out the same information in multiple ways using the
same layout algorithm
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Nodes and links (node-link diagrams); vertices and edges /
arcs
 Direct and indirect ties
 Centrality-peripherality dynamic (degree centrality); closeness-
distance dynamic (paths; degrees of separation)
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Fat (influential) and thin (peripheral) nodes; bridging
nodes
 Nodes are parts of multiple or many networks
 Nodes play different roles in different networks
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Dense networks vs. low-density (sparse) networks
 Networks function better with density for some group
objectives; networks function better with low-density or
sparseness for some other group objectives
 Path dynamics for percolation and flow
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In-group; out-group dynamics; social identity (node,
sub-group, network, and multi-network levels)
Layering effects; network dependencies; network
overlap and interrelationships
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Multimodal elements
 Root entities
 Leaf entities
 Branching connections
 Connective events
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What do you know about penetration (pen)
testing?
Any prior experiences with Maltego
Radium™?
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Uses Java
Runs on Windows, Mac, and Linux operating systems
Applies a 2D or 3D Graphical User Interface (GUI)
Enables complex and fast crawls without need for command line
coding
Uses Maltego Radium™ (by Paterva) Transform Application
Servers for some data extractions
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Shows links between people; groups of people (social
networks); companies; organizations; web sites; internet
infrastructure (domain, DNS names, netblocks, IP
addresses); phrases; affiliations; documents and files
 Based on open-source (publicly available) information or
“open-source intelligence” (OSINT)
 Does not involve the breaking of network controls to access
information
 Assumes benign information in isolation may be turned
malicious in combination and / or relationship to other data (as
in “big data” analytics)
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Is a “dual use” technology with a range of applied “data
harvesting” / structure-mining / datamining and analytical
uses
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“Penetration”: Unauthorized access or a “breakin” to a protected network
 Combination of attacks on hardware (device exploits),
software (malware, password cracking, keyloggers,
and Trojan Horses), and wetware (social engineering,
phishing, and spear phishing)
 Black Box, Gray Box, or Crystal Box (no knowledge of
the target network; partial knowledge of the target
network; full knowledge of the target network)
 Conceptualized and practiced in an adversarial way
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Risk environment modeling with adversaries
(white and gray-hat hackers; red teams)
 Offensive and defensive campaigns (pen testing part
of offensive security testing)
 Countermeasures: security awareness, selfawareness of vulnerabilities (technological, human,
political, policy, and others), policy-setting,
surveillance / intrusion detection, firewalls, training of
staff, security networks, technologies,
communications, professional partnerships, and
others
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Maltego Radium™
 Enables crawls / scrapes / scans of the potential public and
private “attack vectors” of an organization or network’s
structure
 Shows what is seeable and knowable by others, so proper
protections may be put into place (as part of basic
electronic reconnaissance or surveillance of so-called
“perimeter systems”)
 May be used as part of a “red team” simulated (or actual)
attack to test defenses in pen testing
 Offers a starting point for the strategy, planning, further
probes, and other actions
▪ May be followed by more focused, targeted, and nuanced attacks
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“DOXING” (DOCUMENTING)
ATTACKS
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“Doxing” based on
“documenting” by tracking
personally identifiable
information
Creation of “dossiers” of
individuals or groups by
hacktivists to use in ad
hominem and other attacks
CYBER-STALKING
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Tracking individuals’
electronic presences and
relating that to real-world
presences for harassment
and other nefarious
purposes
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INTERPERSONAL ELECTRONIC
SURVEILLANCE (IES)
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Self-surveillance
ORGANIZATIONAL OR GROUP
SURVEILLANCE
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 Electronic grooming
 Analyzing telepresences on social
media platforms through
extractions of Representational
State Transfers or “REST”
 Sousveillance (inverse
surveillance; watchful
vigilance from below or inside
an organization or social
structure; participant
surveillance)
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Horizontal surveillance
Vertical surveillance
Mapping one’s own organization
for public relations purposes
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Perusing Internet and Webbased presences of
organizations
 Creating outreach and marketing
strategies for external
organizations
 Finding identities of individuals for
contact in corporations or
organizations (through the back
door)
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There are legitimate reasons to pursue pseudonymity and
anonymity (such as to prevent harm)
 Eliminating pseudonymity (untraceable long-term anonymity;
exclusive use of a pseudonym over time for reputation transfer,
branding, and “authornym” use; ability to prove “holdership” of a
pseudonym) and anonymity (temporary, ephemeral, and partial
hiding) and enforcing an “inescapable identity” and nondiscretionary revelation
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 Traceability means that at least a single intermediary knows actual
identity (for traceable anonymity or traceable pseudonymity)
 The problem of time involves the fact that archived electronic sites
are fixed (as big data corpuses), and may be analyzed using a variety
of future tools with increasing capabilities
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Making the Internet more of a nonymous, transparent, and
traceable space
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Harder to use Maltego Radium™ for actually verifying
identity and real-ness / personhood, without the
affordances of a verified real-persons database and other
checks
 May guess that a virtual online identity is faked or improperly
back-stopped
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THE INDIVIDUAL EXPERIENCE
 De-anonymizing / re-identification: Connecting
personally identifiable information (PII) of the physical
self to aliases, pseudonyms, handles, or accounts
 Narrowing the potential “anonymity sets” for various
individuals (those to whom one may be temporally
anonymous); the protection of identity as a “layered” one
 Linking partitioned parts of an individual’s online life, and
connecting partial identities (from various contexts) to
coalesce for a fuller version of an individual
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Identifying hidden (inter)relationships in electronic information:
 Showing hidden connections and affiliations (for exploration and
analysis)
▪ Identifying sleeper communities of interest
▪ Identifying influential nodes (or clusters) in a network
 Revealing personal information
▪ Extrapolation of user interests and online seeking behavior
Revelation of potentially private documents
The Human Flaw
 “All aliases initially originate from one person, with one mind, and
one personality.”
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 Tal Z. Zarsky (2004, p. 1352), in “Thinking outside the Box: Considering
Transparency, Anonymity, and Pseudonymity as Overall Solutions to
the Problems of Information Privacy in the Internet Society”…
 Said another way: “Character reveals…”
 Vulnerable to “the aggregation attack” on profiles (requiring only a
few unique data points)
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…using Maltego Radium™
(likely with complementary other software, equipment, and tools)
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Think breadth and depth
Run a Maltego Radium™ Machine (sequencing
including synchronicity of selected “transforms”
through macros)…then further select transforms on
selected nodes
2. Drag and drop from the left menu “palette” to the
work space to actualize different select searches
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Tailoring the data crawl through user filters (selecting
options at various junctures during the crawl)
May layer further queries on former search results (in
the same session or in later sessions)
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Maltego Radium™’s “machines” and “transforms” are
not invisible to the crawled or scanned networks; the
surveiller faces counter-surveillance
 Radium™ user often gives up his or her identity and other
information when conducting a data extraction or crawl
(by leaving trace data)
 Organizations and networks (their network
administrators) have it in their interests to know who is
scoping them out / possibly “prospecting”
▪ Many “attack surfaces” are honeypots (lures / traps / sentinel plots
for hackers to self-reveal); there will be purposeful obfuscation
▪ Forensic analyses post-attack may result much more about the
objectives and criminal skill sets of the attackers
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GENERAL CRAWL
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API key (application
programming interface)
IP Address (Internet Protocol
-- yours or the proxy one you
are using)
The transform executed
The time it executed
The user ID (which gives first
name, last name and email
address)
Paterva does not log the
questions asked or the results
ACCESS TO SOME WEB
SERVICES
First name
Last name
Email address
Time registered
Time first used
How many transform you ran
MAC address you selected
Your operating system type
and version, but not details of
service packs etc.
 GUI version
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User has to allow Paterva to disclaim liabilities
before transform runs may be made
 Crawl “Damage”: Unclear what “damage” may occur
from transforms (but some crawls may be trespassing)
 Sample of a Disclaimer: “Please note this transform is
being run on the Paterva Transform Distribution Server
and has been written by the user 'Andrew MacPherson'.
This transform will be run on * and Paterva cannot be held
responsible for any damage caused by this transform, you
run this AT YOUR OWN RISK. For more information on
this transform feel free to contact…”
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Select machine
(a sequence of
“transforms”)
Identify target
(phrase, name,
URL,
organization,
etc.)
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Select a transform (one type of
information changed to
another type) by dragging and
dropping from left menu bar to
the work space
 Identify target by doubleclicking node
 May highlight a range of icons
to conduct transforms on
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 Sub-transforms customized to
particular types of entities or
nodes
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Information resolves out from
type to type
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Company Stalker: Email addresses at a
company’s domain(s)
 Footprint L1: “Fast” and limited footprint of a
domain
 Footprint L2: “Mild” and semi-limited footprint
of a domain
 Footprint L3: “Intense” and fairly in-depth and
internal footprint of a domain
 Person- Email Address: Identifies a person’s
email addresses (but needs a disambiguated or
fairly uncommon name…or the data is noisy)
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Prune Leaf Entities: Prunes all leaves
(entities with no outgoing links and just one
incoming link—aka pendant nodes) to clear
the screen for re-crawls (and to de-noise the
data)
Twitter Digger: Phrase as a Twitter search
Twitter Geo(graphical) Location: Finding a
person’s location based on multiple
information streams
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Twitter Monitor: Monitors Twitter for
hashtags (#) and named entities mentioned
(@)
 All Twitter crawls rate-limited by amounts of
information downloadable per time period by
Twitter API
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URL to Network and Domain Information:
From URL to network and domain
information
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Devices
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 A phone, mobile device, or
other used by the individual
or connected to various
accounts or a network
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Infrastructure
 AS – Autonomous System
Number (as assigned by IANA
to RIRs)
 DNS Name – Domain Name
System (identification string)
 Domain – Internet Domain
 IPv4 Address – IP version 4
address
Infrastructure (cont.)
 MX Record – DNS mail
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exchanger record (indicator of
mail server accepting email
messages and how email
should be routed through
SMTP)
NS Record – A DNS name
server record (with indicators
of subdomains)
Netblock – An internet
autonomous system
URL – An internet Uniform
Resource Locator (web
address as a character sting)
Website – An internet website
(related web pages served
from a single domain)
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Locations
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 A location on Mother Earth
(to find domains and other
such information)
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Penetration (“Pen”)
Testing
 Company
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Social Network
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Facebook Object
Twit entity
Affiliation – Facebook
Affiliation – Twitter
Personal
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Alias
Document
Email Address
Image (EXIF or “Exchangeable
Image File” data extraction:
geotagged data, GPS, and
general image conditions
information like digital
camera settings)
 Person
 Phone Number
 Phrase
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May import or export
palette contents / entities
(macros for customized
“machines” sequences /
transforms sets, or standalone “transforms”)
 Assumes some ability to
create one’s own scripted
Maltego Radium™ macros
(with Maltego™ Scripting
Language or MSL) as well
 May be as simple as dragand-drop with existing
transforms
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Delinking
 User pruning of nodes that
are not interconnected or
related to the search
 User filtering or
identification of bad
domains to exclude from
the crawl
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Linking
 May link multiple nodes to
run further transforms to
identify possible
relationships
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Extraction of close-in
node-level multiplex
data (vs. meta-level
networks)
 Put cursor on a node
for the details in the
right pane
 May conduct more
transforms on that
node for more data
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May right-click to add notes on various
entities to keep written records and
annotations
Paterva’s Case File enables even more
sophisticated human-annotated recordkeeping of information discoveries (like
research journals or investigator files)
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What have your experiences been with data
visualizations? Graphs?
What are graphs?
How is data used to create graphs?
How are graphs interpreted?
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Layout (and interaction) modes:
Block
Hierarchical
Circular
Organic
Interactive organic
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“Company Stalker” (~ hackerish semantics)
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Person
Affiliation (Flickr)
Email Address
Phone Number
Document
Phrase
Domain
Alias
URL
Website
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Bubble View
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Crawling two
persons to
see if
anything
links up
Combining
crawls to
answer
directed
questions
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Links to an IP address
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Importing:
Saving :
Exporting:
Maltego Radium™ files
Tabular files
.mtgx files
Data sets
Reports
Graphs
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Maltego Radium™ Files
.mtz files (for Palette Transform entities)
.mtgx files (for graph visualizations and
crawls)
Tabular Files
.csv, .xlsx, and .xls (for graphs)
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Saving Proprietary Data Sets
Saves as a .mtgx (Maltego Radium™ graph file)
 May encrypt as AES-128 (Advanced Encryption
Standard 128)
 Native files are not particularly large
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Saves “machine” and “transform” parameters to
re-crawl and update data sets for future runs
Note: Datasets considered to be “unstructured”
or “loosely structured” because of the mix of
content structures among the types of captured
data
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Exporting Reports and
Graphs
 Report file types
(complete summaries
of extracted
information): .pdf
 2D graph as image
(including zoomedin): .gif, .png, .bmp,
and .jpg
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May extract graph data as tables of textual
information for further analysis through “Entity
Lists” tab
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APPLIED DECEPTION
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So far, what do you think is
“knowable” (linkable) using tools
like Maltego Radium™ (along
with other research tools)?
What do you think your online
profile looks like?
How does this knowledge of
Maltego Radium™ capabilities
change how you deceive,
project, hide, obfuscate, or
throw others off your trail
(assuming you might)? 
APPLIED ACADEMIC RESEARCH
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Is there any interest in using this
tool for academic research
applications? If so, what sorts of
research applications are you
considering?
What may be asserted about the
data? How is this data bounded
or limited?
How can this highdimensionality data be used in
an “inference attack”? How
accurate or inaccurate would
such attacks be? How can the
accuracy of such attacks be
improved?
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…through data-mining, structure mining
…through syntactic and semantic stylometry (with writing style as an
“invariant,” with discernible “tells” for obfuscated and imitated writing) for
authorship recognition
…through electronic “tells” and sufficiently detailed individual profiles
…through cross-referencing information from multiple databases (“big data”
analysis, especially statistical correlations)
…through computational research
…through human analytics and logic
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A data crawl as a starting point…
 Interactions with the data
▪ Logical deductions and inferences (e.g. Internet “traffic
analysis”—where people go online—based on linkability
structures)
▪ Ties to physical locations from multiple related accounts
 Pruning of leaf entities to disambiguate the findings
 Additional data extractions and crawls or
computational research
▪ Supplementary research with other complementary or even
overlapping software tools
 Further hypothesizing and testing
 Real-world explorations
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SCALE: DATA SET SIZES
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Giant (macro) data sets
 Forever crawls for the L3
footprints (if one filters
unwisely by being too inclusive;
otherwise, blisteringly fast)
 Total domain searches
(including whole-country
domain searches) but at a high
level
 Huge depth that is timeconsuming to explore
(demanding on researchers)
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Micro data sets as well (to the
level of the individual ego
node)
TIME
May be a slice-in-time,
sequential, or continuously
dynamic (for real-time
dynamic network analysis or
“DNA”; focus on changes over
time or trendline data)
temporal data
 Continuous dynamic for an
“intel dashboard” or “data
feed” for situational
awareness
SPATIO-TEMPORAL
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May be used to link space and
time dimensions
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A fast-changing electronic environment
 Need to update and review data extractions regularly
 Need to be aware of the existence of private channels
 Need to work within an evolving legal ecosystem

Costs: time, computational expense, attention
 Binding up time (even with blinding speeds of
millisecond crawls) and computational expense on
even high-end consumer machines
 Premature crawl stoppages, incomplete crawls, or
over-data (excessive data)
Maltego Radium: Mapping Network Ties and Identities across the Internet
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
User / analyst strengths and limitations
 Need to wield the tool intelligently and not over-claim or





under-claim results
Could use tool for initial discovery, pattern recognition,
and anomaly detection
Engage a fairly high learning curve
Apply complementary data for informed interpretation
Avoid conflating popularity with influence, thin node
peripheral positions with powerlessness, and other
challenges
Avoid under-sampling (collecting too little information)
Maltego Radium: Mapping Network Ties and Identities across the Internet
70

Newness of computational research in some
academic / research / professional fields and
analytical applications
 Challenges to research rigor and generalizability
 Challenges to domain field acceptability
 Openness in terms of methodologies
 The “primitiveness” of network science in various
practical (research, analysis, decision-making, and
other) applications
Maltego Radium: Mapping Network Ties and Identities across the Internet
71

May not be able to generalize far with only a partial
data extraction or crawl (social media platform API
limits, software limits), which provides descriptive
data about networks
 Even relatively “complete” crawls have to be properly
analyzed and documented
▪ Particular “branches” may be analyzed to understand particular ego
neighborhoods or focal nodes
▪ Crawls may include long-closed accounts (such as for emails)
▪ Other branches need to be pruned to de-noise the data
 Analysis requires the making of inferences from what may
be seen structurally
 May only assert within legal bounds (no hackerish
techniques to access information—of course)
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72

Social media platform accounts may be human, ‘bots, or
cyborgs
 Various socio-technical systems (STS) may be gamed
▪ People engage in impression management and spin; they engage in
obfuscation (they are strategic about information); identities may be
back-stopped electronically with various hoaxed accounts
 Electronic systems may all be hoaxed (like honeypots or black
holes, accounts, online email systems, digital contents, and
websites)
 Accounts may have some “light leakage” or “data exhaust”
(unintended revelations that may be observed, analyzed,
inferred, deduced, or extracted by practiced researchers) or
“behavior leakage” (oblique indicators that may be observed
from accounts), but these are often subtle and observed
through machine learning and statistical analyses
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
Maltego Radium™ only captures some information. It
cannot…
 “see” what’s not connected to the Internet and WWW or




capture what is happening non-electronically in the real or
physical or non-cyber world (it cannot bridge the cyberphysical confluence)
“go back in time” to map sites that are no longer online (in
some form)
“see” what is labeled “private” in social media platforms
“see” how users navigate the electronic network
“see” what the characteristics are for particular entities /
nodes / sub-graphs or sub-groups (identify or describe
node “biases” in social network-speak)
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
It cannot…
 “see” what is in the Deep Web or Invisible Web (dynamically




created pages or those requiring registration), only what’s on
the publicly indexable “static” Web
explicitly indicate to researchers which nodes or links to explore
in more depth
maintain a continuous crawl for more dynamic data like Twitter
Digger on background (unless the machine is kept running)
(currently) trace and extract what information is moving
through networks (content diffusion or percolation)
create an invisible or stealth crawl (you will be seen skulking
about)
Maltego Radium: Mapping Network Ties and Identities across the Internet
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
Multimodal data extractions may be done to
understand…




Network ties (social, technological, and content)
Spatiality and geo-location
Technological structures
Human and organizational identity (PII) (through crossreferenced information)
 Device usage online (~ to what Shodan computer search
engine reveals)
 Available contact information

“Not knowing” / being unaware is a “dominated
strategy,” an inherently “losing” or subordinated
approach (in game theory)
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76
Maltego Radium™ brings together various
functionalities that may be done separately with
separate customized programs, Google Search,
Network Solutions’ WHOIS, DNS, NodeXL social
media platform data extractions, and then data
visualization tools… but not as efficiently or as
elegantly (especially for high-scale analyses and link
analysis) …and not continuously over time
 Maltego Radium™’s capabilities may be tested by
conducting “machines” and “transforms” on known
targets with known answers initially
 Maltego Radium™ is styled in a sophisticated way,
with cool visuals and sound effects

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77
MALTEGO RADIUM™ AS A
SOFTWARE TOOL

What are some other possible
practical and “feral” applications
of Maltego Radium™ (adapted
“unintended use” applications)?
 Computational journalism?
Outreach and marketing?
Academic research?
 Predictivity? Is it possible to
predict group dynamics based on
electronic network structures?
Traffic? Contents?

MALTEGO RADIUM™ AS A
PENETRATION TESTING TOOL

In terms of its pen testing
applications, what are some
complementary software
programs that may be used to
 Test network defenses?
 Surface hidden information?
 Identify and exploit vulnerabilities?
What are some new
functionalities that would
enhance this tool?
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78

Paterva
Paterva’s Maltego Radium™
 Maltego Radium
 CaseFile
 Maltego Tungsten (for collaborative data
extractions)*
Maltego Radium Blog
Maltego Tutorials: The Complete and Official Set
(on YouTube)
 Maltego Scripting Language (1.1) Guide (2012)
 Maltego Radium Release (2012)
 Maltego Version 3 User Guide (2011)


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79
COMMUNITY VERSION

Free limited “community”
version available for noncommercial use
COMMERCIAL VERSION

 API keys expire every few
days
 Runs in private or public mode
on community servers (slower
crawls); latter collects backend statistics to benefit the
community
 User information collected
 Lag in features already in the
professional version
Annual subscriptions to the
software license available
(with a 10% educational
discount)
 Initial higher cost ($650 first
year; $350 for consecutive
years thereafter—or 365 days)

Includes access to crawls
using Paterva servers
80





Semantics
Tool functions
Processes
Practical
applications
Worldviews and
mindsets
Drat! No Ctrl + Z “Undo” Function Yet
Maltego Radium: Mapping Network Ties and Identities across the Internet
81
Maltego Radium™ on
Social Media
Maltego Radium: Mapping Network Ties and Identities across the Internet
Paterva on Twitter
(@Paterva)
Maltego on Facebook
Paterva / Maltego on
YouTube
RSS Feed
82
Who is Paterva?
Maltego Radium: Mapping Network Ties and Identities across the Internet
Development Team for
Maltego Radium: 5
individuals based out of
Gauteng, S. Africa
Roelof Temmingh
44B Nelmapius Road Irene
Pretoria, Gauteng 0157
ZA
Phone: +27.27834486996
Email:
[email protected]
om
@roeloftemmingh on
Twitter
A “company stalker” crawl
of www.Paterva.com (to
the right)
Making the company
“drink its own
champagne” :P
83
“A Brief Overview of Social Network Analysis and NodeXL”
Thanks to Dr. Rebecca Gould, who encouraged my learning of
Maltego Radium™ for (totally white-hat) higher education-based
research.
 Thanks to Phyllis Epps, who gave me permission to crawl her
identity @peppslugs on Twitter, for this presentation.
 Thanks to Anibal Pacheco, who gave me permission to crawl his
electronic social networks for this demo. He asked me to share
the following:


 Site: www.anibalpacheco.net
 Account: @anibalpachecoIT on Twitter
 YouTube channel: http://bit.ly/TM8CHP (Megabyte Wizards)
Thanks to CHECK for accepting this presentation and to the
supportive audience!
 The presenter has no tie to nor interests in Paterva.

Maltego Radium: Mapping Network Ties and Identities across the Internet
84






Dr. Shalin Hai-Jew
Instructional Designer, iTAC
212 Hale Library
Kansas State University
785-532-5262
[email protected]
Practically Speaking: No Anonymity
“We may not acknowledge that in an electronic medium, levels
and kinds of anonymity mean, in an important sense, no
anonymity. If there are domains in which we can be anonymous
but those domains are part of a global communication
infrastructure in which there is no anonymity at the entry point,
then it will always be possible to trace someone’s identity.”
 Deborah G. Johnson and Keith Miller’s “Anonymity, Pseudonymity, or Inescapable
Identity on the Net” (1998), Computers and Society
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